About

This canvas is a comprehensive end-to-end sonar mine detection machine learning system that loads the UCI Sonar dataset, performs exploratory analysis and feature profiling, trains and compares multiple classifiers (Random Forest, Extra Trees, Gradient Boosting, Neural Network), and conducts extensive model evaluation through confusion matrices, ROC curves, feature importance analysis, and hyperparameter tuning—with additional unsupervised clustering analysis and synthetic sample prediction demonstrations. The workflow achieves 92.9% accuracy on the test set (with a tuned Extra Trees model reaching 94.4% CV AUC) while providing detailed visualizations of sonar energy profiles, discriminating frequency bands, and model interpretability through permutation importance and individual prediction breakdowns.